To get started, we'll start with an overview of the components and files used in the demonstration. During the demonstration, we walk you through how to build and deploy an Essbase database using Oracle Essbase Studio. Then we talk about how to calculate the database and validate the data in the database using reports. After the demonstration, we discuss some Essbase features you can take advantage of, such as custom load rules and member calculations.

Now that we understand the architecture and components that are used in the different consolidation models, let’s examine some standard deployment issues that need to be addressed. These include security, operational, resource and fault isolation issues as well as scalability and high availability. It is very important to understand that delivery services and the SLAs around those services will drive the actual architecture, design, and implementation. Therefore, architecture, design, and implementation also play directly into the chargeback and metering aspect of the services.

In this section, we discuss the features in Oracle OLAP that you should be aware of when designing analytic workspaces. The content here expands on the concepts introduced in our blogs (where those concepts relate to design) and the general design principles discussed in the preceding section.

Determining Dimensions from user Requirements

As mentioned, user requirements must drive the design of Oracle OLAP cubes. This fact is often overlooked in Oracle OLAP design, as the data is sourced from relational tables or views. Often, these tables are part of a data warehouse with a well-defined structure. The structure of the source tables will be an important influence, but the ultimate structure of the OLAP cubes should be driven by user requirements, not the convenience of loading data from the data warehouse, because often the data warehouse design is not reflective of user requirements. Oracle OLAP cubes can be used solely for their cube-organized materialized views to accelerate performance of queries on data warehouses, but they offer much more.

The IT industry has seen many evolutions, and it is in the midst of another major paradigm shift. Few technologies have captured more attention than big data, and there is tremendous interest in business use cases featuring big data and analytics. Gartner highlighted the top ten technologies and trends that will be strategic for most organizations in 2018. Strategic big data and actionable analytics were among these ten trends. In 2019, Gartner released its top ten IT trends again. This time, the list included mobile, Internet of Things, and smart machines. Big data and analytics become enablers - a hidden force that’s behind the scenes driving these businesses and IT innovations.

When availability is crucial for a business, extremely high levels of disaster tolerance must allow the business to continue in the face of a calamity, without the end users or customers noticing any adverse consequences. The effects of global companies conducting business across time zones spanning “24 × 7 × forever” operations, e-commerce, and the challenges associated with today’s “flat world” all-drive businesses to achieve a level of disaster tolerance capable of ensuring continuous survival and profitability.

Different businesses require different levels of risk with regard to loss of data and potential downtime. A variety of technical solutions can be used to provide varying levels of protection with respect to these business needs. The ideal solutions would have no downtime and allow no data to be lost. Although such solutions do exist, they are expensive, and hence their costs must be weighed against the potential impact of a disaster and its effects on the business.

I remember attending my first Oracle Applications Users Group (OAUG) conference in 1999. I believe this was the year that Larry Ellison, CEO of Oracle Corporation, declared that the internet would change everything. The use of the internet in everyday business life was in full swing at that time and the dot.com boom was roaring with start ups in 90% of all garages in the bay area (slight exaggeration...), some of which had turned into enormously successful corporations. Indeed the internet has changed life throughout the world. Most of us arc just as lost without internet access as wc arc without our cell phones.

The advent and evolution of ERP (Enterprise Resource Planning) systems have had and are still having almost as significant an impact on organizations as the internet has had on the individual. ERP systems such as SAP and Oracle’s suite of products (E-Business Suite (EBS), PeopleSoft, JD Edwards, Siebel, Hyperion, Fusion, etc) continue to cause a dramatic change in business and IT processes.

Structured Query Language (SQL) is a language that has been widely accepted and adopted for accessing relational databases. This language allows users to perform database operations such as reading, creating, modifying, and deleting the data. Since the summer of 1970, when Dr. E.F. Codd published the paper A Relational Model of Data for Large Shared Data Banks for the ACM journal, the language has matured comprehensively as an industry standard. With its broad range of features and easy adaptation to enterprise environments, the SQL language has been typically regarded as the most reliable language for interacting with relational databases.

Oracle's regular expression support manifests itself in the form of three SQL functions and one predicate that you can use to search and manipulate text in any of Oracle's supported text datatypes: VARCHAR2, CHAR, NVARCHAR2, NCHAR, CLOB, and NCLOB.

Regular expression support does not extend to LONG, because LONG is supported only for backward compatibility with existing code.

The amount of data being generated is on the verge of an explosion, and according to an International Data Corporation (IDC) 2012 report, the total amount of data stored by corporations globally would surpass a zettabyte (1 zettabyte = 1 billion terabytes) by the end of 2012. Therefore, it is critical for the data companies to be prepared with an infrastructure that can store and analyze extremely large datasets, and be able to generate actionable intelligence that in turn can drive business decisions. Oracle offers a broad portfolio of products to help enterprises acquire, manage, and integrate big data with existing corporate data, and perform rich and intelligent analytics.